ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (5): 1078-1089.doi: 10.7544/issn1000-1239.2018.20160681

• 图形图像 • 上一篇    下一篇

基于图和稀疏主成分分析的多目标显著性检测

梁大川1,李静1,刘赛2,李东民1   

  1. 1(南京航空航天大学计算机科学与技术学院 南京 211106); 2(南瑞集团有限公司(国网电力科学研究院有限公司) 南京 210003) (dacliang@nuaa.edu.cn)
  • 出版日期: 2018-05-01
  • 基金资助: 
    国家电网公司科技项目

Multiple Object Saliency Detection Based on Graph and Sparse Principal Component Analysis

Liang Dachuan1, Li Jing1, Liu Sai2,Li Dongmin1   

  1. 1(College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106); 2(NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 210003)
  • Online: 2018-05-01

摘要: 针对具有多个显著目标且背景较为复杂的图像,提出了一种基于全连接图和稀疏主成分分析(sparse principal component analysis, sPCA)的显著性检测方法.首先,在不同的尺度空间上利用目标先验知识快速获取包含预选显著目标的空间位置信息,同时,在超像素分割的基础上构造全连接图,并计算超像素级的显著图.然后,利用目标先验知识提取并优化超像素显著图的显著性区域,采用稀疏主成分分析提取优化后的显著性像素点的主要特征,获取相应尺度的显著图.最后,将多个尺度下的显著图进行融合得到最终的显著图.该方法充分利用了超像素与像素显著性计算的优势,在提高检测速度的同时获得更高的检测精度.在公开的多目标数据集SED2和HKU_IS上进行实验验证,结果表明:该方法能够有效检测出复杂背景下的多个显著目标.

关键词: 显著性检测, 全连接图, 稀疏主成分分析, 目标先验, 超像素分割

Abstract: In order to detect multiple salient objects from the image with cluttered background, a new multi-object salient detection method based on fully connected graph and sparse principal component analysis is proposed. Firstly, a rapid coarse detection method with different scales is adopted to obtain the object prior with the location of candidate objects and the pixel level saliency map. Meanwhile, we construct a fully connected graph based on the superpixel segmentation to obtain the superpixel-level saliency map. The salient regions are extracted from the superpixel-level binarized salient object prior map and a sparse principal component analysis method is used to gain the main features vector from the pixel matrix composed of the pixels in the optimized salient regions and obtain the salient map of corresponding scale. Finally, the final salient map is fused with the multi-scale saliency maps. Our method takes the advantage of pixel and superpixel method, it can not only simplify the calculation but also improve the detection precision of the salient objects in the image. Quantitative experiments on two public datasets SED2 and HKU_IS demonstrate that out method can detect multiple salient objects from complex images and outperforms other state-of-the-art methods.

Key words: saliency detection, fully connected graph, sparse principal component analysis, object prior, superpixel segmentation

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